# Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network

arXiv: https://arxiv.org/abs/1703.05320 · HTML: https://mlsanity.com/paper/1703.05320/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 9 of 9)

- [JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System](https://mlsanity.com/paper/2503.14258/) (2025)
- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [Case law retrieval: problems, methods, challenges and evaluations in the last 20 years](https://mlsanity.com/paper/2202.07209/) (2022)
- [STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals](https://mlsanity.com/paper/2406.15313/) (2024)
- [Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code](https://mlsanity.com/paper/2112.03033/) (2021)
- [GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering](https://mlsanity.com/paper/2412.04119/) (2024)
- [Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network](https://mlsanity.com/paper/2105.06083/) (2021)
- [Using attention methods to predict judicial outcomes](https://mlsanity.com/paper/2207.08823/) (2022)
- [Exploring the State of the Art in Legal QA Systems](https://mlsanity.com/paper/2304.06623/) (2023)

## Abstract (arXiv)

This paper presents a study of employing Ranking SVM and Convolutional Neural Network for two missions: legal information retrieval and question answering in the Competition on Legal Information Extraction/Entailment. For the first task, our proposed model used a triple of features (LSI, Manhattan, Jaccard), and is based on paragraph level instead of article level as in previous studies. In fact, each single-paragraph article corresponds to a particular paragraph in a huge multiple-paragraph article. For the legal question answering task, additional statistical features from information retrieval task integrated into Convolutional Neural Network contribute to higher accuracy.